25 research outputs found
A statistical atlas-based technique for automatic segmentation of the first Heschls gyrus in human auditory cortex from MR images
Abstract-We present an automatic method for the segmentation of the first transverse temporal gyrus of Heschl (HG), the morphological marker for primary auditory cortex in humans. The proposed technique utilizes a statistical anatomical atlas of the gyrus, generated from a set of training samples using principal component analysis. The training set consists of MRI data from 12 subjects with the corresponding Heschl's gyri manually labeled in each hemisphere (separate atlases were generated for each hemisphere). We used a leave-oneout approach to automatically segment Heschl's gyri in both hemispheres from the MR image data using generated atlases. We assessed the accuracy of this atlas-based technique by using it to segment the HG region from several test cases and finding the overlap between the segmented and labeled HG regions. Results demonstrated more than 75% and 83% accuracy in the extraction of the HG volumes in the left and right hemispheres, respectively. It is expected that the proposed tool can be adapted to extract other anatomical regions in the brain
Optimal operation of stand-alone microgrid considering emission issues and demand response program using whale optimization Algorithm
Microgrids are new technologies for integrating renewable energies into power systems. Optimal operation of renewable energy sources in standalone micro-grids is an intensive task due to the continuous variation of their output powers and intermittant nature. This work addresses the optimum operation of an independent microgrid considering the demand response program (DRP). An energy management model with two different scenarios has been proposed to minimize the costs of operation and emissions. Interruptible/curtailable loads are considered in DRPs. Besides, due to the growing concern of the developing efficient optimization methods and algorithms in line with the increasing needs of microgrids, the focus of this study is on using the whale meta-heuristic algorithm for operation management of microgrids. The findings indicate that the whale optimization algorithm outperforms the other known algorithms such as imperialist competitive and genetic algorithms, as well as particle swarm optimization. Furthermore, the results show that the use of DRPS has a significant impact on the costs of operation and emissions
Advanced simulation methods for occupant-centric building design
Performance quantification through simulation has been particularly advantageous to building design, as it can be applied to non-existent buildings in the design process, allows for testing design variants under identical conditions, and demands much less resources as compared to physical measurements. Consequently, use of building simulation in the design process has evolved to – for example – establish and verify design performance, screen and optimize design parameters, and study robustness and adaptability in adverse conditions. In this context, the present chapter investigates how the state-of-the-art simulation-aided design procedures can contribute to realize occupant-centric design objectives. To this end, the chapter, first, discusses the ways in which simulation-aided design methods can represent occupants and capture their interactions with buildings’ environmental control systems. Subsequently, a number of key simulation-aided design methods and objectives are explored with a focus on the role of occupants. Finally, a carefully described prototypical building model serves to demonstrate and test the introduced occupant-centric simulation-aided design procedures
Neuropsychosocial profiles of current and future adolescent alcohol misusers
A comprehensive account of the causes of alcohol misuse must accommodate individual differences in biology, psychology and environment, and must disentangle cause and effect. Animal models1 can demonstrate the effects of neurotoxic substances; however, they provide limited insight into the psycho-social and higher cognitive factors involved in the initiation of substance use and progression to misuse. One can search for pre-existing risk factors by testing for endophenotypic biomarkers2 in non-using relatives; however, these relatives may have personality or neural resilience factors that protect them from developing dependence3. A longitudinal study has potential to identify predictors of adolescent substance misuse, particularly if it can incorporate a wide range of potential causal factors, both proximal and distal, and their influence on numerous social, psychological and biological mechanisms4. Here we apply machine learning to a wide range of data from a large sample of adolescents (n = 692) to generate models of current and future adolescent alcohol misuse that incorporate brain structure and function, individual personality and cognitive differences, environmental factors (including gestational cigarette and alcohol exposure), life experiences, and candidate genes. These models were accurate and generalized to novel data, and point to life experiences, neurobiological differences and personality as important antecedents of binge drinking. By identifying the vulnerability factors underlying individual differences in alcohol misuse, these models shed light on the aetiology of alcohol misuse and suggest targets for prevention
Johnsrude IS. Reducing intersubject anatomical variation: Effect of normalization method on sensitivity of functional magnetic resonance imaging data analysis in auditory cortex and the superior temporal region
Abstract Conventional group analysis of functional MRI (fMRI) data usually involves spatial alignment of anatomy across participants by registering every brain image to an anatomical reference image. Due to the high degree of inter-subject anatomical variability, a low-resolution average anatomical model is typically used as the target template, and/or smoothing kernels are applied to the fMRI data to increase the overlap among subjects' image data. However, such smoothing can make it difficult to resolve small regions such as subregions of auditory cortex when anatomical morphology varies among subjects. Here, we use data from an auditory fMRI study to show that using a high-dimensional registration technique (HAMMER) results in an enhanced functional signal-to-noise ratio (fSNR) for functional data analysis within auditory regions, with more localized activation patterns. The technique is validated against DARTEL, a high-dimensional diffeomorphic registration, as well as against commonly used low-dimensional normalization techniques such as the techniques provided with SPM2 (cosine basis functions) and SPM5 (unified segmentation) software packages. We also systematically examine how spatial resolution of the template image and spatial smoothing of the functional data affect the results. Only the high-dimensional technique (HAMMER) appears to be able to capitalize on the excellent anatomical resolution of a single-subject reference template, and, as expected, smoothing increased fSNR, but at the cost of spatial resolution. In general, results demonstrate significant improvement in fSNR using HAMMER * Corresponding author. Address: Medical Image Analysis Laboratory, School of Computing, Queen's University, Kingston, ON, CANADA. Tel: +1 (613) 533 2797. Email address: [email protected] (Amir M. Tahmasebi ) May 19, 2009 compared to analysis after normalization using DARTEL, or conventional normalization such as cosine basis function and unified segmentation in SPM, with more precisely localized activation foci, at least for activation in the region of auditory cortex. Preprint submitted to NeuroImag
A New Approach for Creating Customizable Cytoarchitectonic Probabilistic Maps without a Template
Experimental Identification and Analysis of the Dynamics of a PHANToM Premium 1.5A Haptic Device
Optimal Operation of Stand-Alone Microgrid Considering Emission Issues and Demand Response Program Using Whale Optimization Algorithm
Microgrids are new technologies for integrating renewable energies into power systems. Optimal operation of renewable energy sources in standalone micro-grids is an intensive task due to the continuous variation of their output powers and intermittent nature. This work addresses the optimum operation of an independent microgrid considering the demand response program (DRP). An energy management model with two different scenarios has been proposed to minimize the costs of operation and emissions. Interruptible/curtailable loads are considered in DRPs. Besides, due to the growing concern of the developing efficient optimization methods and algorithms in line with the increasing needs of microgrids, the focus of this study is on using the whale meta-heuristic algorithm for operation management of microgrids. The findings indicate that the whale optimization algorithm outperforms the other known algorithms such as imperialist competitive and genetic algorithms, as well as particle swarm optimization. Furthermore, the results show that the use of DRPS has a significant impact on the costs of operation and emissions